Hybrid model for early identification post-Covid-19 sequelae
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.26/46914 |
Resumo: | Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation. |
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Hybrid model for early identification post-Covid-19 sequelaeCovid-19Machine-learningVerbal decision analysisHybrid modelMedical diagnostic optimizationDecision support systemsArtificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.SpringerRepositório Comumde Andrade, Evandro CarvalhoPinheiro, Luana Ibiapina C. C.Pinheiro, Plácido RogérioNunes, Luciano CominPinheiro, Mirian Calíope DantasPereira, Maria Lúcia DuarteAbreu, WilsonFilho, Raimir HolandaSimão Filho, MarumPinheiro, Pedro Gabriel C. D.Nunes, Rafael Espíndola Comin2023-10-02T09:28:54Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.26/46914engde Andrade, E.C., Pinheiro, L.I.C.C., Pinheiro, P.R., Pereira, M.LD., Abreu, W., Filho, R,H., Filho, M.S., Pinheiro, P.G., Nunes, R.E.C.(2023) Hybrid model for early identification post-Covid-19 sequelae. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-023-04555-310.1007/s12652-023-04555-31868-5145info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-10-05T09:09:23Zoai:comum.rcaap.pt:10400.26/46914Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:33:24.838930Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Hybrid model for early identification post-Covid-19 sequelae |
title |
Hybrid model for early identification post-Covid-19 sequelae |
spellingShingle |
Hybrid model for early identification post-Covid-19 sequelae de Andrade, Evandro Carvalho Covid-19 Machine-learning Verbal decision analysis Hybrid model Medical diagnostic optimization Decision support systems |
title_short |
Hybrid model for early identification post-Covid-19 sequelae |
title_full |
Hybrid model for early identification post-Covid-19 sequelae |
title_fullStr |
Hybrid model for early identification post-Covid-19 sequelae |
title_full_unstemmed |
Hybrid model for early identification post-Covid-19 sequelae |
title_sort |
Hybrid model for early identification post-Covid-19 sequelae |
author |
de Andrade, Evandro Carvalho |
author_facet |
de Andrade, Evandro Carvalho Pinheiro, Luana Ibiapina C. C. Pinheiro, Plácido Rogério Nunes, Luciano Comin Pinheiro, Mirian Calíope Dantas Pereira, Maria Lúcia Duarte Abreu, Wilson Filho, Raimir Holanda Simão Filho, Marum Pinheiro, Pedro Gabriel C. D. Nunes, Rafael Espíndola Comin |
author_role |
author |
author2 |
Pinheiro, Luana Ibiapina C. C. Pinheiro, Plácido Rogério Nunes, Luciano Comin Pinheiro, Mirian Calíope Dantas Pereira, Maria Lúcia Duarte Abreu, Wilson Filho, Raimir Holanda Simão Filho, Marum Pinheiro, Pedro Gabriel C. D. Nunes, Rafael Espíndola Comin |
author2_role |
author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Comum |
dc.contributor.author.fl_str_mv |
de Andrade, Evandro Carvalho Pinheiro, Luana Ibiapina C. C. Pinheiro, Plácido Rogério Nunes, Luciano Comin Pinheiro, Mirian Calíope Dantas Pereira, Maria Lúcia Duarte Abreu, Wilson Filho, Raimir Holanda Simão Filho, Marum Pinheiro, Pedro Gabriel C. D. Nunes, Rafael Espíndola Comin |
dc.subject.por.fl_str_mv |
Covid-19 Machine-learning Verbal decision analysis Hybrid model Medical diagnostic optimization Decision support systems |
topic |
Covid-19 Machine-learning Verbal decision analysis Hybrid model Medical diagnostic optimization Decision support systems |
description |
Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-10-02T09:28:54Z 2023 2023-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.26/46914 |
url |
http://hdl.handle.net/10400.26/46914 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
de Andrade, E.C., Pinheiro, L.I.C.C., Pinheiro, P.R., Pereira, M.LD., Abreu, W., Filho, R,H., Filho, M.S., Pinheiro, P.G., Nunes, R.E.C.(2023) Hybrid model for early identification post-Covid-19 sequelae. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-023-04555-3 10.1007/s12652-023-04555-3 1868-5145 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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